Machine Learning Operations (MLOps) platform

Unlock the value of machine learning in production

Ease collaboration between data scientists and IT/Ops

Bringing machine learning in production is more difficult than just training ML models and deploying them as APIs for prediction. Only a small percentage of ML projects make it to production because of deployment complexity, lack of governance tools and many other reasons. Once in production, ML models often fail to adapt to the changes in the environment and its dynamic data which results in performance degradation.

To maintain the prediction accuracy of ML models in production, an active monitoring of model performance is mandatory. This allows to know when to retrain it using the most recent data and the newest implementation techniques, then redeploy in production.

To achieve this virtuous circle, an established CI/CD (continuous integration/continuous delivery), as well as continuous model training, suited for ML systems, is necessary. Deploying an ML pipeline that can automate the retraining and deployment of new models will help you adapt to rapid changes in your data and business environment.

Machine Learning Open Studio (MLOS) product from Activeeon helps data scientists and IT operations work together in an MLOps approach allowing to bring ML models to production. It simplifies machine learning application lifecycle management providing end-to-end orchestration, automation and scalability.

Machine learning model lifecycle automation

MLOps covers the whole machine learning (or deep learning) lifecycle: model generation (ML development lifecycle, continuous integration/continuous delivery), orchestration and deployment, monitoring and analytics. You can deploy, monitor, and manage machine learning models in production, then govern their use in production environments.

Machine Learning Open Studio (MLOS) from Activeeon enables a repeatable and scalable machine learning lifecycle to lower complexity of AI for fast delivery. It helps you create adaptable pipelines in order to work with dynamic models.

How to integrate machine learning in current automation processes?

You don’t have to immediately move all of your processes from manual to fully automated pipelines. You can gradually implement MLOps practices to improve the automation of your ML system development and production.